docs: def decode_prob(self, class_probabilities):
          """Given predicted class probabilites for a set of examples, annotate
          each logit with a class name.
  
          By default, we name each class using its index in the logits array.
  
          Args:
              class_probabilities (array): Class probabilities as output by
                  `self.predict`, i.e., a numpy array of shape (num_examples,
                  num_classes).
  
          Returns:
              Annotated class probabilities for each input example, as a list of
              dicts where each dict is formatted as:
                  {
                      'index': class_index,
                      'name': class_name,
                      'prob': class_probability
                  }
  
          """
          results = []
          for row in class_probabilities:
              entries = []
              for i, prob in enumerate(row):
                  entries.append({'index': i,
                                  'name': str(i),
                                  'prob': prob})
  
              entries = sorted(entries,
                               key=itemgetter('prob'),
                               reverse=True)[:self.top_probs]
  
              for entry in entries:
                  entry['prob'] = '{:.3f}'.format(entry['prob'])
              results.append(entries)
          return results